TSNet:A Two-stage Network for Image Dehazing with Multi-scale Fusion and Adaptive Learning

• URL: http://arxiv.org/abs/2404.02460v1
• Date: Wed, 3 Apr 2024 05:02:46 GMT
• Title: TSNet:A Two-stage Network for Image Dehazing with Multi-scale Fusion and Adaptive Learning
• Authors: Xiaolin Gong, Zehan Zheng, Heyuan Du,
• Abstract summary: This paper proposes a two-stage image dehazing network called TSNet. It consists of the multi-scale fusion module (MSFM) and the adaptive learning module (ALM) Extensive experiments demonstrate that TSNet exhibits superior dehazing performance on both synthetic and real-world datasets.
• Score: 0.37240490024629924
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Image dehazing has been a popular topic of research for a long time. Previous deep learning-based image dehazing methods have failed to achieve satisfactory dehazing effects on both synthetic datasets and real-world datasets, exhibiting poor generalization. Moreover, single-stage networks often result in many regions with artifacts and color distortion in output images. To address these issues, this paper proposes a two-stage image dehazing network called TSNet, mainly consisting of the multi-scale fusion module (MSFM) and the adaptive learning module (ALM). Specifically, MSFM and ALM enhance the generalization of TSNet. The MSFM can obtain large receptive fields at multiple scales and integrate features at different frequencies to reduce the differences between inputs and learning objectives. The ALM can actively learn of regions of interest in images and restore texture details more effectively. Additionally, TSNet is designed as a two-stage network, where the first-stage network performs image dehazing, and the second-stage network is employed to improve issues such as artifacts and color distortion present in the results of the first-stage network. We also change the learning objective from ground truth images to opposite fog maps, which improves the learning efficiency of TSNet. Extensive experiments demonstrate that TSNet exhibits superior dehazing performance on both synthetic and real-world datasets compared to previous state-of-the-art methods.

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